
"""simple docstring"""


from manim import *



class 	__a	(   _lowerCAmelCase						):






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		Rectangle(height=0.5							,   width=0.5      )
              UpperCamelCase       			=		Rectangle(height=0.46							,   width=0.46      ).set_stroke(width=0      )

              UpperCamelCase       			=		[mem.copy() for i in range(6      )]
              UpperCamelCase       			=		[mem.copy() for i in range(6      )]
              UpperCamelCase       			=		VGroup(*UpperCAmelCase_      ).arrange(UpperCAmelCase_							,   buff=0      )
              UpperCamelCase       			=		VGroup(*UpperCAmelCase_      ).arrange(UpperCAmelCase_							,   buff=0      )
              UpperCamelCase       			=		VGroup(UpperCAmelCase_							,   UpperCAmelCase_      ).arrange(UpperCAmelCase_							,   buff=0      )
              UpperCamelCase       			=		Text("CPU"							,   font_size=24      )
              UpperCamelCase       			=		Group(UpperCAmelCase_							,   UpperCAmelCase_      ).arrange(UpperCAmelCase_							,   buff=0.5							,   aligned_edge=UpperCAmelCase_      )
              cpu.move_to([-2.5, -0.5, 0]      )
              self.add(UpperCAmelCase_      )

              UpperCamelCase       			=		[mem.copy() for i in range(1      )]
              UpperCamelCase       			=		VGroup(*UpperCAmelCase_      ).arrange(UpperCAmelCase_							,   buff=0      )
              UpperCamelCase       			=		Text("GPU"							,   font_size=24      )
              UpperCamelCase       			=		Group(UpperCAmelCase_							,   UpperCAmelCase_      ).arrange(UpperCAmelCase_							,   buff=0.5							,   aligned_edge=UpperCAmelCase_      )
              gpu.align_to(UpperCAmelCase_							,   UpperCAmelCase_      )
              gpu.set_x(gpu.get_x() - 1      )

              self.add(UpperCAmelCase_      )

              UpperCamelCase       			=		[mem.copy() for i in range(6      )]
              UpperCamelCase       			=		VGroup(*UpperCAmelCase_      ).arrange(UpperCAmelCase_							,   buff=0      )

              UpperCamelCase       			=		Text("Model"							,   font_size=24      )
              UpperCamelCase       			=		Group(UpperCAmelCase_							,   UpperCAmelCase_      ).arrange(UpperCAmelCase_							,   buff=0.5							,   aligned_edge=UpperCAmelCase_      )
              model.move_to([3, -1.0, 0]      )

              self.play(
                  Create(UpperCAmelCase_							,   run_time=1      )							,   Create(UpperCAmelCase_							,   run_time=1      )							,   Create(UpperCAmelCase_							,   run_time=1      )							,   )

              UpperCamelCase       			=		MarkupText(
                  f"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."							,   font_size=24							,   )

              UpperCamelCase       			=		Square(side_length=2.2      )
              key.move_to([-5, 2, 0]      )

              UpperCamelCase       			=		MarkupText(
                  f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"							,   font_size=18							,   )

              key_text.move_to([-5, 2.4, 0]      )

              step_a.move_to([2, 2, 0]      )
              self.play(Write(UpperCAmelCase_							,   run_time=2.5      )							,   Write(UpperCAmelCase_      )							,   Write(UpperCAmelCase_      )      )

              self.add(UpperCAmelCase_      )

              UpperCamelCase       			=		[]
              UpperCamelCase       			=		[]
              UpperCamelCase       			=		[]
              for i, rect in enumerate(UpperCAmelCase_      ):
                     UpperCamelCase       			=		Rectangle(height=0.46							,   width=0.46      ).set_stroke(width=0.0      ).set_fill(UpperCAmelCase_							,   opacity=0.7      )
                     cpu_target.move_to(UpperCAmelCase_      )
                     cpu_target.generate_target()
                     UpperCamelCase       			=		0.46 / 4
                     UpperCamelCase       			=		0.46 / 3

                     if i == 0:
                            cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      )							,   buff=0.02							,   direction=UpperCAmelCase_      )
                            cpu_target.target.set_x(cpu_target.target.get_x() + 0.1      )
                     elif i == 3:
                            cpu_target.target.next_to(cpu_targs[0].target							,   direction=UpperCAmelCase_							,   buff=0.0      )
                     else:
                            cpu_target.target.next_to(cpu_targs[i - 1].target							,   direction=UpperCAmelCase_							,   buff=0.0      )
                     cpu_targs.append(UpperCAmelCase_      )

                     first_animations.append(rect.animate(run_time=0.5      ).set_stroke(UpperCAmelCase_      )      )
                     second_animations.append(MoveToTarget(UpperCAmelCase_							,   run_time=1.5      )      )

              self.play(*UpperCAmelCase_      )
              self.play(*UpperCAmelCase_      )

              self.wait()







"""simple docstring"""


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       List[str]				=      ['''image_processor''', '''tokenizer''']
       UpperCamelCase_		:       Dict				=      '''BlipImageProcessor'''
       UpperCamelCase_		:       Optional[int]				=      '''AutoTokenizer'''






       def __init__(	self			:				List[str]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				List[str]							,   UpperCAmelCase_			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              super().__init__(UpperCAmelCase_							,   UpperCAmelCase_      )

              # add QFormer tokenizer
              UpperCamelCase       			=		qformer_tokenizer






       def __call__(	self			:				Tuple							,   UpperCAmelCase_			:				ImageInput = None							,   UpperCAmelCase_			:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				Union[bool, str, PaddingStrategy] = False							,   UpperCAmelCase_			:				Union[bool, str, TruncationStrategy] = None							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				int = 0							,   UpperCAmelCase_			:				Optional[int] = None							,   UpperCAmelCase_			:				Optional[bool] = None							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				bool = True							,   UpperCAmelCase_			:				Optional[Union[str, TensorType]] = None							,   **UpperCAmelCase_			:				str							,   )->	BatchFeature:







              """simple docstring"""
              if images is None and text is None:
                     raise ValueError("You have to specify at least images or text."      )

              UpperCamelCase       			=		BatchFeature()

              if text is not None:
                     UpperCamelCase       			=		self.tokenizer(
                         text=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   padding=UpperCAmelCase_							,   truncation=UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   stride=UpperCAmelCase_							,   pad_to_multiple_of=UpperCAmelCase_							,   return_attention_mask=UpperCAmelCase_							,   return_overflowing_tokens=UpperCAmelCase_							,   return_special_tokens_mask=UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_							,   return_length=UpperCAmelCase_							,   verbose=UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
                     encoding.update(UpperCAmelCase_      )
                     UpperCamelCase       			=		self.qformer_tokenizer(
                         text=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   padding=UpperCAmelCase_							,   truncation=UpperCAmelCase_							,   max_length=UpperCAmelCase_							,   stride=UpperCAmelCase_							,   pad_to_multiple_of=UpperCAmelCase_							,   return_attention_mask=UpperCAmelCase_							,   return_overflowing_tokens=UpperCAmelCase_							,   return_special_tokens_mask=UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_							,   return_length=UpperCAmelCase_							,   verbose=UpperCAmelCase_							,   return_tensors=UpperCAmelCase_							,   **UpperCAmelCase_							,   )
                     UpperCamelCase       			=		qformer_text_encoding.pop("input_ids"      )
                     UpperCamelCase       			=		qformer_text_encoding.pop("attention_mask"      )

              if images is not None:
                     UpperCamelCase       			=		self.image_processor(UpperCAmelCase_							,   return_tensors=UpperCAmelCase_      )
                     encoding.update(UpperCAmelCase_      )

              return encoding






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   *UpperCAmelCase_			:				Optional[int]							,   **UpperCAmelCase_			:				List[str]      )->	Dict:







              """simple docstring"""
              return self.tokenizer.batch_decode(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   *UpperCAmelCase_			:				Dict							,   **UpperCAmelCase_			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              return self.tokenizer.decode(*UpperCAmelCase_							,   **UpperCAmelCase_      )






       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		self.tokenizer.model_input_names
              UpperCamelCase       			=		self.image_processor.model_input_names
              return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   UpperCAmelCase_			:				Tuple							,   **UpperCAmelCase_			:				Tuple      )->	Union[str, Any]:







              """simple docstring"""
              if os.path.isfile(UpperCAmelCase_      ):
                     raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"      )
              os.makedirs(UpperCAmelCase_							,   exist_ok=UpperCAmelCase_      )
              UpperCamelCase       			=		os.path.join(UpperCAmelCase_							,   "qformer_tokenizer"      )
              self.qformer_tokenizer.save_pretrained(UpperCAmelCase_      )
              return super().save_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )






       @classmethod
       def 						_SCREAMING_SNAKE_CASE							(	cls			:				Dict							,   UpperCAmelCase_			:				int							,   **UpperCAmelCase_			:				int      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		AutoTokenizer.from_pretrained(UpperCAmelCase_							,   subfolder="qformer_tokenizer"      )
              UpperCamelCase       			=		cls._get_arguments_from_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )
              args.append(UpperCAmelCase_      )
              return cls(*UpperCAmelCase_      )





